Triple

T16405533
Position Surface form Disambiguated ID Type / Status
Subject AirAsia E398414 entity
Predicate subsidiary P258 FINISHED
Object Indonesia AirAsia E296230 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Indonesia AirAsia | Statement: [AirAsia, subsidiary, Indonesia AirAsia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Indonesia AirAsia
Context triple: [AirAsia, subsidiary, Indonesia AirAsia]
  • A. AirAsia Indonesia chosen
    AirAsia Indonesia is a low-cost airline based in Indonesia and a subsidiary of the Malaysia-based AirAsia Group, operating domestic and international flights across Asia.
  • B. AirAsia
    AirAsia is a Malaysian low-cost airline known for its extensive network of domestic and international routes across Asia and beyond.
  • C. Thai AirAsia
    Thai AirAsia is a Thai low-cost airline operating domestic and international flights, and is part of the wider AirAsia group based in Southeast Asia.
  • D. Philippines AirAsia
    Philippines AirAsia is a low-cost airline based in the Philippines and a subsidiary of the AirAsia Group, operating domestic and international flights across Asia.
  • E. Malindo Air
    Malindo Air is a Malaysian hybrid full-service and low-cost airline that became the first operator of the Boeing 737 MAX 8.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d87f2950248190bc8ad9b9bebdc8c8 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e327d2b4e48190b7153f198639e9cd completed April 18, 2026, 6:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00581245108190842cfd68ec640236 completed May 10, 2026, 10:04 a.m.
Created at: April 10, 2026, 5:09 a.m.